Hou, Zhonglin (East China Normal University), Molesworth, Brett (UNSW Sydney), Zhang, Yonggang (Tsinghua University), Molloy, Oleksandra (UNSW Canberra), Guo, Jingjing (Xidian University), Li, Joel (UNSW Sydney), Liu, Hong (East China Normal University)

External Driver Classification Using Reservoir Computing Enhancing Automated Vehicle Safety

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Abstract

Ensuring the safety of Autonomous Vehicles (AVs) in mixed traffic environments is a considerable challenge due to the unpredictable behaviors and diverse driving styles of human drivers. This paper introduces a novel framework for driver classification of the surrounding vehicles from the external viewpoint of AVs, utilizing Reservoir Computing (RC) and Transfer Learning (TL) with observable data such as speed, acceleration, and speed limits, preserving driver privacy. Privacy feature augmentation based on TL generates comprehensive characteristics from both source and target domains, while time-series data augmentation increases classification accuracy within a small-time window. Augmented feature metrics are processed by an RC-based classifier to predict driver characteristics. Performance analysis shows the F1-score can reach up to 0.997, and comparison studies confirm the framework achieves state-of-the-art performance. The experiments demonstrate the ability of the framework to enhance the accuracy and reliability of driver classification, improving the real-time adaptability of AVs in complex traffic scenarios.